國立高雄大學圖資館 |

Language: English

Back

Modelling and control of dynamic sys...

Kocijan, Jus.

Modelling and control of dynamic systems using Gaussian process models

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modelling and control of dynamic systems using Gaussian process modelsby Jus Kocijan.
Author:	Kocijan, Jus.
Published:	Cham :Springer International Publishing :2016.
Description:	xvi, 267 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Gaussian processes.
Online resource:	http://dx.doi.org/10.1007/978-3-319-21021-6
ISBN:	9783319210216$q(electronic bk.)

Modelling and control of dynamic systems using Gaussian process models
Kocijan, Jus.

Modelling and control of dynamic systems using Gaussian process models[electronic resource] /by Jus Kocijan. - Cham :Springer International Publishing :2016. - xvi, 267 p. :ill., digital ;24 cm. - Advances in industrial control,1430-9491. - Advances in industrial control..

System Identification with GP Models -- Incorporation of Prior Knowledge -- Control with GP Models -- Trends, Challenges and Research Opportunities -- Case Studies.

This monograph opens up new horizons for engineers and researchers in academia and in industry dealing with or interested in new developments in the field of system identification and control. It emphasizes guidelines for working solutions and practical advice for their implementation rather than the theoretical background of Gaussian process (GP) models. The book demonstrates the potential of this recent development in probabilistic machine-learning methods and gives the reader an intuitive understanding of the topic. The current state of the art is treated along with possible future directions for research. Systems control design relies on mathematical models and these may be developed from measurement data. This process of system identification, when based on GP models, can play an integral part of control design in data-based control and its description as such is an essential aspect of the text. The background of GP regression is introduced first with system identification and incorporation of prior knowledge then leading into full-blown control. The book is illustrated by extensive use of examples, line drawings, and graphical presentation of computer-simulation results and plant measurements. The research results presented are applied in real-life case studies drawn from successful applications including: a gas-liquid separator control; urban-traffic signal modelling and reconstruction; and prediction of atmospheric ozone concentration. A MATLAB® toolbox, for identification and simulation of dynamic GP models is provided for download. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

ISBN: 9783319210216$q(electronic bk.)

Standard No.: 10.1007/978-3-319-21021-6doiSubjects--Topical Terms:

211434
Gaussian processes.

LC Class. No.: QA274.4

Dewey Class. No.: 519.2

Modelling and control of dynamic systems using Gaussian process models
LDR:03065nmm a2200325 a 4500 001 481553
003 DE-He213
005 20160720153704.0
006 m d
007 cr nn 008maaau
008 161007s2016 gw s 0 eng d
020 $a 9783319210216$q(electronic bk.)
020 $a 9783319210209$q(paper)
024 7 $a 10.1007/978-3-319-21021-6 $2 doi
035 $a 978-3-319-21021-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA274.4
072 7 $a TJFM $2 bicssc
072 7 $a TEC004000 $2 bisacsh
082 0 4 $a 519.2 $2 23
090 $a QA274.4 $b .K76 2016
100 1 $a Kocijan, Jus. $3 737680
245 1 0 $a Modelling and control of dynamic systems using Gaussian process models $h [electronic resource] / $c by Jus Kocijan.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xvi, 267 p. : $b ill., digital ; $c 24 cm.
490 1 $a Advances in industrial control, $x 1430-9491
505 0 $a System Identification with GP Models -- Incorporation of Prior Knowledge -- Control with GP Models -- Trends, Challenges and Research Opportunities -- Case Studies.
520 $a This monograph opens up new horizons for engineers and researchers in academia and in industry dealing with or interested in new developments in the field of system identification and control. It emphasizes guidelines for working solutions and practical advice for their implementation rather than the theoretical background of Gaussian process (GP) models. The book demonstrates the potential of this recent development in probabilistic machine-learning methods and gives the reader an intuitive understanding of the topic. The current state of the art is treated along with possible future directions for research. Systems control design relies on mathematical models and these may be developed from measurement data. This process of system identification, when based on GP models, can play an integral part of control design in data-based control and its description as such is an essential aspect of the text. The background of GP regression is introduced first with system identification and incorporation of prior knowledge then leading into full-blown control. The book is illustrated by extensive use of examples, line drawings, and graphical presentation of computer-simulation results and plant measurements. The research results presented are applied in real-life case studies drawn from successful applications including: a gas-liquid separator control; urban-traffic signal modelling and reconstruction; and prediction of atmospheric ozone concentration. A MATLAB® toolbox, for identification and simulation of dynamic GP models is provided for download. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
650 0 $a Gaussian processes. $3 211434
650 0 $a System analysis. $3 182013
650 0 $a Control theory. $3 182248
650 1 4 $a Engineering. $3 210888
650 2 4 $a Control. $3 349080
650 2 4 $a Industrial Chemistry/Chemical Engineering. $3 273974
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 348605
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Advances in industrial control. $3 558134
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-21021-6
950 $a Engineering (Springer-11647)